diff --git a/012验证yolov8ONNX模型.py b/012验证yolov8ONNX模型.py
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+import cv2
+import numpy as np
+import onnxruntime as ort
+
+class YOLOv8ONNX:
+    def __init__(self, model_path, conf_threshold=0.5, iou_threshold=0.4):
+        """
+        初始化YOLOv8 ONNX模型
+        
+        Args:
+            model_path: ONNX模型文件路径
+            conf_threshold: 置信度阈值
+            iou_threshold: NMS IoU阈值
+        """
+        self.conf_threshold = conf_threshold
+        self.iou_threshold = iou_threshold
+        
+        # 创建ONNX Runtime会话
+        self.session = ort.InferenceSession(model_path)
+        
+        # 获取模型输入输出信息
+        self.input_name = self.session.get_inputs()[0].name
+        self.output_name = self.session.get_outputs()[0].name
+        
+        # 获取输入尺寸
+        input_shape = self.session.get_inputs()[0].shape
+        self.input_height = input_shape[2]
+        self.input_width = input_shape[3]
+        
+    def preprocess(self, image):
+        """
+        预处理图像
+        
+        Args:
+            image: 输入图像 (BGR格式)
+            
+        Returns:
+            preprocessed_image: 预处理后的图像
+            scale_ratio: 缩放比例
+            pad_info: 填充信息 (pad_x, pad_y)
+        """
+        # 获取原图尺寸
+        h, w = image.shape[:2]
+        
+        # 计算缩放比例
+        scale = min(self.input_height / h, self.input_width / w)
+        new_h, new_w = int(h * scale), int(w * scale)
+        
+        # 等比例缩放
+        resized_image = cv2.resize(image, (new_w, new_h))
+        
+        # 计算填充
+        pad_x = (self.input_width - new_w) // 2
+        pad_y = (self.input_height - new_h) // 2
+        
+        # 创建填充后的图像
+        padded_image = np.full((self.input_height, self.input_width, 3), 114, dtype=np.uint8)
+        padded_image[pad_y:pad_y + new_h, pad_x:pad_x + new_w] = resized_image
+        
+        # 转换为模型输入格式: BGR -> RGB, HWC -> CHW, 归一化
+        input_image = padded_image[:, :, ::-1].transpose(2, 0, 1).astype(np.float32) / 255.0
+        input_image = np.expand_dims(input_image, axis=0)  # 添加batch维度
+        
+        return input_image, scale, (pad_x, pad_y)
+    
+    def postprocess(self, outputs, scale, pad_info, original_shape):
+        """
+        后处理模型输出 - 针对YOLOv8格式优化
+        
+        Args:
+            outputs: 模型原始输出
+            scale: 图像缩放比例
+            pad_info: 填充信息
+            original_shape: 原图尺寸
+            
+        Returns:
+            boxes: 检测框 [[x1, y1, x2, y2], ...]
+            scores: 置信度分数
+            class_ids: 类别ID
+        """
+        predictions = outputs[0]  # 形状通常是: [1, 6, 8400] 或 [1, num_classes+4, num_boxes]
+        
+        # YOLOv8输出格式: [batch, 4+num_classes, num_boxes]
+        # 需要转置为 [batch, num_boxes, 4+num_classes]
+        if len(predictions.shape) == 3:
+            predictions = predictions.transpose(0, 2, 1)  # [1, num_boxes, 4+num_classes]
+        
+        predictions = predictions[0]  # 移除batch维度: [num_boxes, 4+num_classes]
+        
+        # 打印调试信息
+        # print(f"预测输出形状: {predictions.shape}")
+        # print(f"前几个预测值: {predictions[:5]}")
+        
+        # 分离坐标和分类信息
+        boxes = predictions[:, :4]  # [x_center, y_center, width, height]
+        scores = predictions[:, 4:]  # 类别置信度 [num_boxes, num_classes]
+        
+        # print(f"检测框形状: {boxes.shape}")
+        # print(f"分数形状: {scores.shape}")
+        
+        # 获取最高置信度和对应类别
+        class_ids = np.argmax(scores, axis=1)
+        confidences = np.max(scores, axis=1)
+        
+        # print(f"置信度范围: {confidences.min():.4f} - {confidences.max():.4f}")
+        # print(f"检测到的类别: {np.unique(class_ids)}")
+        
+        # 过滤低置信度检测
+        valid_indices = confidences > self.conf_threshold
+        valid_boxes = boxes[valid_indices]
+        valid_confidences = confidences[valid_indices]
+        valid_class_ids = class_ids[valid_indices]
+        
+        # print(f"过滤后检测数量: {len(valid_boxes)}")
+        
+        if len(valid_boxes) == 0:
+            return [], [], []
+        
+        # 转换为 [x1, y1, x2, y2] 格式
+        x_center, y_center, width, height = valid_boxes[:, 0], valid_boxes[:, 1], valid_boxes[:, 2], valid_boxes[:, 3]
+        x1 = x_center - width / 2
+        y1 = y_center - height / 2
+        x2 = x_center + width / 2
+        y2 = y_center + height / 2
+        
+        converted_boxes = np.stack([x1, y1, x2, y2], axis=1)
+        
+        # 坐标反变换到原图
+        pad_x, pad_y = pad_info
+        converted_boxes[:, [0, 2]] = (converted_boxes[:, [0, 2]] - pad_x) / scale
+        converted_boxes[:, [1, 3]] = (converted_boxes[:, [1, 3]] - pad_y) / scale
+        
+        # 限制坐标范围
+        h, w = original_shape[:2]
+        converted_boxes[:, [0, 2]] = np.clip(converted_boxes[:, [0, 2]], 0, w)
+        converted_boxes[:, [1, 3]] = np.clip(converted_boxes[:, [1, 3]], 0, h)
+        
+        # 非极大值抑制 (NMS)
+        indices = cv2.dnn.NMSBoxes(
+            converted_boxes.tolist(), 
+            valid_confidences.tolist(), 
+            self.conf_threshold, 
+            self.iou_threshold
+        )
+        
+        if len(indices) > 0:
+            indices = indices.flatten()
+            return converted_boxes[indices], valid_confidences[indices], valid_class_ids[indices]
+        
+        return [], [], []
+    
+    def detect(self, image):
+        """
+        对图像进行目标检测
+        
+        Args:
+            image: 输入图像 (BGR格式)
+            
+        Returns:
+            boxes: 检测框列表
+            scores: 置信度分数列表
+            class_ids: 类别ID列表
+        """
+        # 预处理
+        input_image, scale, pad_info = self.preprocess(image)
+        
+        # 推理
+        outputs = self.session.run([self.output_name], {self.input_name: input_image})
+        
+        # 后处理
+        boxes, scores, class_ids = self.postprocess(outputs, scale, pad_info, image.shape)
+        
+        return boxes, scores, class_ids
+
+def draw_detections(image, boxes, scores, class_ids, class_names=None):
+    """
+    在图像上绘制检测结果
+    
+    Args:
+        image: 输入图像
+        boxes: 检测框
+        scores: 置信度分数
+        class_ids: 类别ID
+        class_names: 类别名称列表（可选）
+    
+    Returns:
+        绘制了检测结果的图像
+    """
+    result_image = image.copy()
+    
+    for i, (box, score, class_id) in enumerate(zip(boxes, scores, class_ids)):
+        x1, y1, x2, y2 = map(int, box)
+        
+        # 绘制边界框
+        cv2.rectangle(result_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        
+        # 准备标签文本
+        if class_names and class_id < len(class_names):
+            label = f"{class_names[class_id]}: {score:.2f}"
+        else:
+            label = f"Class {class_id}: {score:.2f}"
+        
+        # 绘制标签背景
+        label_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)[0]
+        cv2.rectangle(result_image, (x1, y1 - label_size[1] - 10), 
+                     (x1 + label_size[0], y1), (0, 255, 0), -1)
+        
+        # 绘制标签文本
+        cv2.putText(result_image, label, (x1, y1 - 5), 
+                   cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
+    
+    return result_image
+
+# # 使用示例
+# if __name__ == "__main__":
+#     # 1. 初始化检测器 (降低置信度阈值进行测试)
+#     detector = YOLOv8ONNX("path/to/your/model.onnx", conf_threshold=0.25, iou_threshold=0.45)
+    
+#     # 2. 加载图像
+#     image = cv2.imread("path/to/your/image.jpg")
+    
+#     # 3. 进行检测
+#     boxes, scores, class_ids = detector.detect(image)
+    
+#     # 4. 定义你的两个类别名称 (根据训练时的类别顺序调整)
+#     class_names = ["class_0", "class_1"]  # 替换为你的实际类别名称
+    
+#     # 5. 打印检测结果
+#     print(f"检测到 {len(boxes)} 个目标:")
+#     for i, (box, score, class_id) in enumerate(zip(boxes, scores, class_ids)):
+#         class_name = class_names[class_id] if class_id < len(class_names) else f"unknown_{class_id}"
+#         print(f"  {i+1}: {class_name} - 置信度: {score:.3f} - 位置: {box}")
+    
+#     # 6. 绘制结果
+#     result_image = draw_detections(image, boxes, scores, class_ids, class_names)
+    
+#     # 7. 显示或保存结果
+#     cv2.imshow("Detection Result", result_image)
+#     cv2.waitKey(0)
+#     cv2.destroyAllWindows()
+    
+#     # 保存结果
+#     # cv2.imwrite("result.jpg", result_image)
+# # 使用示例
+if __name__ == "__main__":
+    # 1. 初始化检测器
+    detector = YOLOv8ONNX("/home/admin-root/haotian/康达瑞贝斯机器狗/yolov8_20250820.onnx", conf_threshold=0.25, iou_threshold=0.45)
+    
+    # 2. 加载图像
+    image = cv2.imread("/home/admin-root/haotian/康达瑞贝斯机器狗/YoloV8Obj/dataset_20250819/train/images/1e4c75b76e531606e2adc491a8f09ae8_frame_000140.jpg")
+    
+    # 3. 进行检测
+    boxes, scores, class_ids = detector.detect(image)
+    print(boxes, scores, class_ids)
+    
+    # 4. 可选：定义类别名称
+    class_names = ["extinguish", "other"]  # 根据你的模型调整
+    
+    # 5. 打印检测结果
+    print(f"检测到 {len(boxes)} 个目标:")
+    for i, (box, score, class_id) in enumerate(zip(boxes, scores, class_ids)):
+        class_name = class_names[class_id] if class_id < len(class_names) else f"unknown_{class_id}"
+        print(f"  {i+1}: {class_name} - 置信度: {score:.3f} - 位置: {box}")
+    
+    # 5. 绘制结果
+    result_image = draw_detections(image, boxes, scores, class_ids, class_names)
+    
+    # 6. 显示或保存结果
+    # cv2.imshow("Detection Result", result_image)
+    # cv2.waitKey(0)
+    # cv2.destroyAllWindows()
+    
+    # 保存结果
+    cv2.imwrite("result_yolov8.jpg", result_image)
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